Prior Stages Research Articles

A Critique of Stanley and Williamson's Intellectualist Account of Skill

One prominent intellectualist position in the debate on the nature of skill, famously defended by Stanley and Williamson (2001; 2017), claims that skill and knowing-how are reducible to knowledgethat. To defend this claim, Stanley and Williamson argue that skill and knowledge-that develop in a sufficiently similar way through different learning stages. In this paper we offer a novel argument to reject this version of intellectualism on methodological, descriptive, and conceptual grounds. We do so by drawing on the work of Heidegger, Dreyfus, and Ryle on skill. We first offer a descriptive account of skilful action based on the work developed by these authors, and we then move on to show that skill and knowledge-that exhibit significant differences. First, while skills cannot be imparted merely linguistically, some forms of knowledge-that can be exclusively imparted and learned verbally. Second, weshow that the temporal learning curves of skill and knowledge-that are importantly different: the former usually requires a more or less extensive training process, whereas the latter may be acquired in a relatively sudden fashion. Finally, we show that, while the structure of most forms of knowledge-that is aggregative, the structure of skilful understanding is dynamic and holistic, which entails that prior skill stages cannot be retrieved and re-enacted at will.

HUMAN THOUGHT PROCESS ON HOTEL ADVERTISEMENTS:

Conceptual Blending (CB) is a framework within Cognitive Linguistics to create meaning construction that determines brain cognitive functions upon reading hotel advertisements which the study aimed to illustrate and elaborate. The study utilized advancement theory of CB by applying intertextuality concept to create multiple meanings done from prior studies. The study also utilized qualitative method to analyze a poster from ANA Intercontinental Manza Beach Resort through applying non-participant observation, purposive sampling, and triangulation theory methods. To analyze the data, the concepts of data collection, data condensation, data display and data conclusion were used. The study found that the processes of composition, completion, and elaboration within blended space of CB built the meaning construction within human brain. Composition stage created arrays of features from composing features within input spaces that build up new concepts within completion stage. The completion stage then created new concepts based on cultural knowledge combining all the features composed within composition process and the meanings were broaden by elaboration stage which cleared out what the new concepts actually meant for. These processes created the emergent structure which carries novelty meanings from the analysis. In this stage, the meaning has been completely concluded from all the elaborated meaning happening on prior stages.

Recovery of lithium from spent NMC batteries through water leaching, carbothermic reduction, and evaporative crystallization process

The demand for lithium grows exponentially due to its expanding range of applications. Because of their composition, spent lithium-ion batteries represent a promising secondary source of lithium. The present work focuses on a two-stage lithium recovery process applied to two different black masses from NMC 622 electric vehicle batteries after prior crushing and separation stages. In the first stage, hydrolysis is used to extract lithium compounds from the degradation products of the electrolyte (LiPF6). In the second stage, a carbothermic reduction process is carried out in which various reducing agents are evaluated. The result of this stage is a product that is leached with water and crystallised by evaporation. The result of the first stage is two industrially useful compounds: LiF, which is used in the production of lithium-ion battery electrolytes, and Li3PO4, which is used in the production of lithium-iron-phosphate batteries. In the second stage, Li2CO3 is recovered with a remarkably low LiF content, with a purity higher than 98.4 %. This process allows 90–94 % of the total lithium in the black mass to be recovered.

TemporalAugmenter: An Ensemble Recurrent Based Deep Learning Approach for Signal Classification

Ensemble modeling has been widely used to solve complex problems as it helps to improve overall performance and generalization. In this paper, we propose a novel TemporalAugmenter approach based on ensemble modeling for augmenting the temporal information capturing for long-term and short-term dependencies in data integration of two variations of recurrent neural networks in two learning streams to obtain the maximum possible temporal extraction. Thus, the proposed model augments the extraction of temporal dependencies. In addition, the proposed approach reduces the preprocessing and prior stages of feature extraction, which reduces the required energy to process the models built upon the proposed TemporalAugmenter approach, contributing towards green AI. Moreover, the proposed model can be simply integrated into various domains including industrial, medical, and human-computer interaction applications. Our proposed approach empirically evaluated the speech emotion recognition, electrocardiogram signal, and signal quality examination tasks as three different signals with varying complexity and different temporal dependency features.

Longitudinal patterns of natural hazard exposures and anxiety and depression symptoms among young adults in four low- and middle-income countries

We estimated the effect of community-level natural hazard exposure during prior developmental stages on later anxiety and depression symptoms among young adults and potential differences stratified by gender. We analyzed longitudinal data (2002–2020) on 5585 young adults between 19 and 26 years in Ethiopia, India, Peru, and Vietnam. A binary question identified community-level exposure, and psychometrically validated scales measured recent anxiety and depression symptoms. Young adults with three exposure histories (“time point 1,” “time point 2,” and “both time points”) were contrasted with their unexposed peers. We applied a longitudinal targeted minimum loss-based estimator with an ensemble of machine learning algorithms for estimation. Young adults living in exposed communities did not exhibit substantially different anxiety or depression symptoms from their unexposed peers, except for young women in Ethiopia who exhibited less anxiety symptoms (average causal effect [ACE] estimate = − 8.86 [95% CI: − 17.04, − 0.68] anxiety score). In this study, singular and repeated natural hazard exposures generally were not associated with later anxiety and depression symptoms. Further examination is needed to understand how distal natural hazard exposures affect lifelong mental health, which aspects of natural hazards are most salient, how disaster relief may modify symptoms, and gendered, age-specific, and contextual differences.

Soft Tissue Reconstruction After Sacral Neoplasm Resection: The University of California San Francisco Experience.

Resection of sacral neoplasms such as chordoma and chondrosarcoma with subsequent reconstruction of large soft tissue defects is a complex multidisciplinary process. Radiotherapy and prior abdominal surgery play a role in reconstructive planning; however, there is no consensus on how to maximize outcomes. In this study, we present our institution's experience with the reconstructive surgical management of this unique patient population. We conducted a retrospective review of patients who underwent reconstruction after resection of primary or recurrent pelvic chordoma or chondrosarcoma between 2002 and 2019. Surgical details, hospital stay, and postoperative outcomes were assessed. Patients were divided into 3 groups for comparison based on reconstruction technique: gluteal-based flaps, vertical rectus abdominus myocutaneous (VRAM) flaps, and locoregional fasciocutaneous flaps. Twenty-eight patients (17 males, 11 females), with mean age of 62 years (range, 34-86 years), were reviewed. Twenty-two patients (78.6%) received gluteal-based flaps, 3 patients (10.7%) received VRAM flaps, and 3 patients (10.7%) were reconstructed with locoregional fasciocutaneous flaps. Patients in the VRAM group were significantly more likely to have undergone total sacrectomy (P < 0.01) in a 2-stage operation (P < 0.01) compared with patients in the other 2 groups. Patients in the VRAM group also had a significantly greater average number of reoperations (2 ± 3.5, P = 0.04) and length of stay (29.7 ± 20.4 days, P = 0.01) compared with the 2 other groups. The overall minor and major wound complication rates were 17.9% and 42.9%, respectively, with 17.9% of patients experiencing at least 1 infection or seroma. There was no association between prior abdominal surgery, surgical stages, or radiation therapy and an increased risk of wound complications. Vertical rectus abdominus myocutaneous flaps are a more suitable option for patients with larger defects after total sacrectomy via 2-staged anteroposterior resections, whereas gluteal myocutaneous flaps are effective options for posterior-only resections. For patients with small- to moderate-sized defects, local fasciocutaneous flaps are a less invasive and effective option. Paraspinous flaps may be used in combination with other techniques to provide additional bulk and coverage for especially long postresection wounds. Furthermore, mesh is a useful adjunct for any reconstruction aimed at protecting against intra-abdominal complications.

Match Pathways and College Graduation: A Longitudinal and Multidimensional Framework for Academic Mismatch

Academic mismatch, the incompatibility between applicants’/students’ aptitude and their desired/current academic program, is considered a key predictor of degree attainment. Evaluations of this link tend to be cross-sectional, however, focusing on specific stages of the college pipeline and ignoring mismatch at prior or later stages and their potential outcomes. We developed and tested a longitudinal and multidimensional framework that classifies mismatches along the college pipeline by direction (match, overmatch, undermatch) and stage (application, admission, enrollment). We combined them into match pathways and evaluated how these configurations shape graduation outcomes. Analyses of administrative data on all applicants and students at universities in Israel between 1998 and 2003 demonstrate the added value of this framework. We show that academic mismatch is substantially more prevalent and complex than previously depicted, with only a third of all students fully matched at all stages. Mismatch at each stage affects graduation chances, but the effect is also path-dependent. Thus, it is important to study the entire match pathway to understand how academic mismatch shapes inequality in graduation outcomes. Our findings have important implications for policies designed to increase degree attainment and diversity.

MSIE-Net: Associative Entity-Based Multi-Stage Network for Structured Information Extraction from Reports

Efficient document recognition and sharing remain challenges in the healthcare, insurance, and finance sectors. One solution to this problem has been the use of deep learning techniques to automatically extract structured information from paper documents. Specifically, the structured extraction of a medical examination report (MER) can enhance medical efficiency, data analysis, and scientific research. While current methods focus on reconstructing table bodies, they often overlook table headers, leading to incomplete information extraction. This paper proposes MSIE-Net (multi-stage-structured information extraction network), a novel structured information extraction method, leveraging refined attention transformers and associated entity detection to enhance comprehensive MER information retrieval. MSIE-Net includes three stages. First, the RVI-LayoutXLM (refined visual-feature independent LayoutXLM) targets key information extraction. In this stage, the refined attention accentuates the interaction between different modalities by adjusting the attention score at the current position using previous position information. This design enables the RVI-LayoutXLM to learn more specific contextual information to improve extraction performance. Next, the associated entity detection module, RIFD-Net (relevant intra-layer fine-tuned detection network), identifies each test item’s location within the MER table body. Significantly, the backbone of RIFD-Net incorporates the intra-layer feature adjustment module (IFAM) to extract global features while homing in on local areas, proving especially sensitive for inspection tasks with dense and long bins. Finally, structured post-processing based on coordinate aggregation links the outputs from the prior stages. For the evaluation, we constructed the Chinese medical examination report dataset (CMERD), based on real medical scenarios. MSIE-Net demonstrated competitive performance in tasks involving key information extraction and associated entity detection. Experimental results validate MSIE-Net’s capability to successfully detect key entities in MER and table images with various complex layouts, perform entity relation extraction, and generate structured labels, laying the groundwork for intelligent medical documentation.

Using Memristor Element to Improve the Reliability of Digital Circuits

Memristor has been used as a filter stage to improve The reliability of digital circuits has been improved by using memristor. This element works as a filter stage to waive most of the glitches generated in prior stages. The proposed technique is applied to three different circuits to improve their reliability. Those circuits are: a chain of inverters represents a long path topology, c432 benchmark represents a short path circuit, c1908 benchmark represents a long path circuit. The memristor stage is connected between the last two stages of each output path of the circuit. The memristance of the memristor is changed using a certain parameter (x0), higher memristance means longer propagation delay and more glitches are attenuated. The experiment is run at different values of the memristance and the error probability is calculated at each value. The effect of this stage is studied by calculating the energy consumption and performance of the circuits with and without this stage. The Reliability-Energy-Performance trade-off relationship is found and introduced for each circuit, the results show that the reliability of the circuit is improved to the highest levels, when

Improved Patient Adherence to Family-Based Helicobacter pylori Infection Control and Management Strategy in Central China and Its Influencing Factors.

Patient adherence status to the newly introduced family-based Helicobacter pylori (H. pylori) infection control and management strategy remains unclear, so are its influencing factors. We aim to investigate family members' adherence and its influencing factors during the family-based H. pylori infection management practice for related disease prevention. Based on our previously family-based H. pylori survey in 2021, 282 families including 772 individuals were followed up 2 years after the initial survey to compare if the investigation and education might improve family member's adherence. The participant's adherence to H. pylori infection awareness, retest, treatment, publicity, gastroscopy, and hygiene habits were followed up, and their influencing factors were also analyzed. The overall participant's adherence to recommendations on H. pylori awareness, retest, treatment, publicity, gastroscopy, and hygiene habits were 77% (187/243), 67.3% (138/205), 60.1% (211/351), 46.5% (107/230), 45.6% (159/349), and 39.1% (213/545), respectively; and all showed improvements compared with their prior survey stages. The top reasons for rejection to treatment, retest, and gastroscopy were forgetting or unaware of H. pylori infection (30.3%), busy (32.8%), and asymptomatic (67.9%), respectively. Independent risk factor for low adherence to treatment was occupation (e.g., staff: OR 4.49, 95% CI 1.34-15.10). Independent favorable factors for treatment adherence were individuals at the ages of 18-44 years (OR 0.19, 95% CI 0.04-0.89) and had a large family size (e.g., four family members: OR 0.15, 95% CI 0.06-0.41); for retest adherence, it was individuals at the ages of 60-69 years (OR 0.23, 95% CI 0.06-0.97); for gastroscopy adherence, it was individuals at the age of 60-69 years (OR 0.46, 95% CI 0.28-0.75), and with gastrointestinal symptoms (OR 0.57, 95% CI 0.36-0.90). Family-based H. pylori management increases individual adherence to treatment, retest, and awareness, and there are also improved adherence to gastroscopy, publicity, and personal hygiene recommendations; further efforts are required to enhance the individual adherence rate for related disease prevention.

Characterizing hydraulic fracture growth using distributed acoustic sensing-recorded microseismic reflections

Hydraulic fracturing is crucial for enhancing hydrocarbon production from unconventional reservoirs. The characterization of fracture geometry and propagation has significant value in understanding reservoir response and designing more efficient completions. Distributed acoustic sensing (DAS) is a rapidly developing technology that can be used for this purpose because it provides wide-aperture observations of microseismic wavefields that contain direct P and S arrivals as well as converted and reflected waves. In addition to traditional approaches for microseismic event location and source mechanism analysis, the high spatial resolution of DAS microseismic recordings allows the imaging of induced fractures with reflected waves. Reflections are generated by waves radiated from microseismic events that impinge on hydraulic fractures created during prior treatment stages. We use a straightforward method based on f- k filtering and ray tracing to map reflected S waves from the time domain to reflectivity in the space domain. A case study of fracture imaging indicates that inferred fracture development, based on reflection imaging, is consistent with fracture-driven interactions observed using low-frequency DAS (LF-DAS) data. In addition, this study reveals reflection images of apparent distal fractures that do not reach the fiber and thus are not directly observed by LF-DAS. Fracture images obtained from several microseismic events during the same stage provide the opportunity to observe snapshots of dynamic fracture evolution processes.

Deep Learning to Detect the Function of Calmodulin in Alzheimer’s Disease

Alzheimer’s disease (AD) is marked by losses in cognitive functions. Hallmarks of AD, amyloid beta (Aβ) plaques and neurofibrillary tangles, are intercellular structures that are highly associated with AD. The accumulations of these molecules could be seen as results of other events in prior stages of AD. The calcium hypothesis was first proposed to explain the forming of these hallmarks while the calmodulin (CaM) hypothesis was proposed to provide more improved insight later. The CaM hypothesis states that the uncontrolled flux of calcium ions in the neurons is the cause of neurodegeneration. In addition, the critical role of CaM and CaM-binding proteins (CaMBPs) are identified in the hypothesis. In this project, the regulating role of CaM and CaM-related protein in AD are discussed, including CaMBPs, critical receptors and risk factors. Besides, the potential therapeutic target related to CaM are also mentioned to provide a promise direction of treatment for AD.

Pain of local anesthetic injection of lidocaine during subsequent stages of Mohs micrographic surgery: A multicenter prospective cohort study

Patients awake during staged cutaneous surgery procedures may experience procedure-related pain. To determine whether the level of pain associated with local anesthetic injections prior to each Mohs stage increases with subsequent Mohs stages. Multicenter longitudinal cohort study. Patients rated pain (visual analog scale: 1-10) after anesthetic injection preceding each Mohs stage. Two hundred fifty-nine adult patients presenting for Mohs who required multiple Mohs stages at 2 academic medical centers were enrolled; 330 stages were excluded due to complete anesthesia from prior stages, and 511 stages were analyzed. Mean visual analog scale pain ratings were nominally but not significantly different for subsequent stages of Mohs surgery (stage 1: 2.5; stage 2: 2.5; stage 3: 2.7: stage 4:2.8: stage 5: 3.2; P=.770). Between 37% and 44% experienced moderate pain, and 9.5% and 12.5% severe pain, during first as versus subsequent stages (P>.05) LIMITATIONS: Both academic centers were in urban areas. Pain rating is inherently subjective. Patients did not report significantly increased anesthetic injection pain level during subsequent stages of Mohs.

Software Fault Prediction for Imbalanced Data: A Survey on Recent Developments

The method of recognizing faults in a software system is acknowledged as software fault prediction. Software faults predicted in prior stages help in the management of resources and time required during software testing and maintenance. The identified software module can be fixed ahead of time, saving time and money near the end of the software development process. Over the years, various supervised machine learning-based techniques for fault prediction have been suggested. These models’ accuracy is based on the training datasets. The models are created and trained using a labeled dataset consisting of multiple independent variables like lines of codes, the complexity of the software, the size of the software, etc., and a dependent binary variable, either true or false. But the fault dataset may have some concerns like a class overlapping problem, class imbalance problem, null values, etc. Recent research in software fault prediction focuses on data quality. An imbalanced dataset is one in which one of the class data is present in the majority and another class data is present in the minority. Models built using imbalanced datasets are biased which results in inaccurate predictions. Therefore, balancing the dataset is important. In this paper, the most recent software fault prediction algorithms, which focus on class imbalance issues are discussed. A comparative presentation is presented in this paper, which would benefit the scholar in selecting the best techniques of fault prediction based on different datasets and algorithms. According to the survey, SMOTE is the most commonly used data sampling technique for dealing with data quality issues.

The Qixiangzhan eruption, Changbaishan-Tianchi volcano, China/DPRK: new age constraints and their implications

Zircon double dating (ZDD) of comendite lava reveals an eruption age of 7.0 ± 0.9 ka for the Qixiangzhan eruption (QXZ), Changbaishan-Tianchi volcano, China/DPRK. This age is supported by new 40Ar/39Ar sanidine experiments and a previous age control from charcoal at the base of the QXZ. The revised age supports correlations with distal ash in Eastern China and Central Japan and establishes a significant (estimated at Volcanic Explosivity Index 5+) eruption that may provide a useful Holocene stratigraphic marker in East Asia. The new age indicates that the QXZ lava does not record a ca. 17 ka Hilina Pali/Tianchi geomagnetic field excursion but rather a heretofore unrecognized younger Holocene excursion at ca. 7–8 ka. Comparison between U–Th zircon crystallization and ZDD as well as 40Ar/39Ar sanidine ages indicates a protracted period of accumulation of the QXZ magma that extends from ca. 18 ka to the eruption age. This connotes an eruption that mixed remobilized early formed crystals (antecrysts) from prior stages of magma accumulation with crystals formed near the time of eruption. Based on these results, a recurrence rate of ca. 7–8 ka for the Changbaishan-Tianchi magma system is found over the last two major eruption cycles.

SmartCare: A Symptoms Based Disease Prediction Model Using Machine Learning Approach

Abstract: The breakthrough on computer-based technology has resulted in storage of a lot of electronic data in the healthcare industry. Machine Learning technology has been proven beneficial in giving an immeasurable platform in the medical field so that health care issues can be resolved effortlessly and expeditiously. Prediction of disease at early stage could help people from getting the necessary treatment on time. These days many virtual prediction models are available for the same. The existing systems either made use of only one algorithm or prediction system were capable for predicting only one disease. The maximum accuracy of the existing systems range between 52% to 88%. The algorithms used in various prediction system consisted of Linear Regression, Decision Tree, Naïve Bayes, KNN, CNN, Random Forest Tree, etc. In our project i.e., “SmartCare: A Symptoms Based Disease Prediction Model Using Machine Learning Approach”, it is possible to predict more than one disease at a time. So, the user does not need to traverse many models to predict the diseases. It will help to reduce the time and cost of predicting diseases at prior stages, so as to prevent the extremities of it and thus, there is a chance of reducing mortality rate.

Protocol for the development of a reporting guideline for causal and counterfactual prediction models in biomedicine

IntroductionWhile there are guidelines for reporting on observational studies (eg, Strengthening the Reporting of Observational Studies in Epidemiology, Reporting of Studies Conducted Using Observational Routinely Collected Health Data Statement), estimation...

Management Strategies During the COVID-19 Pandemic Crisis: The Experiences of Health Managers from Iran, Ardabil Province.

The COVID-19 outbreak is the most threatening public health challenge in the 21th century, and more than 200 countries are affected. Considering that Iran was one of the first countries influenced by the COVID-19 pandemic, this study aimed to explain the crisis management strategies during the COVID-19 pandemic in Ardabil province. This study employed a qualitative method using content analysis in which 12 healthcare managers or decision makers involved in the management of the COVID-19 crisis were recruited through purposeful sampling. In-depth, semi-structured interviews were used to collect data which continued until data saturation. Data analysis led to 9 categories, including prior preparation for the COVID-19 crisis; challenges and management of workforce shortages; benefiting from the participation of volunteer staff; challenges and strategies for physical space, supplies, and PPE; designation of referral centers for COVID-19; protocolized patient transport; benefiting from donations and charity support; management of information about COVID-19; and learning from the prior stages of crisis. This study revealed that in critical situations, managers use multiple and, to some extent, unique strategies for decision making and crisis control. Therefore, the health system can use the findings of the current study for proper response to similar crises and training of future managers.

An Anticipation and Categorization of Lung Cancer using Machine Learning Method

Medical services area is completely not the same as other modern area deduce from the worth of human existence and individuals gives the most elevated need. In all the illness that have existed in humanity cellular breakdown in the lungs has arisen as one of the most fata one over and over. Additionally, it is one of the most widely recognized and adding to passing among every one of the malignant growths. Instances of cellular breakdown in the lungs are expanding quickly. There are around 75,000 cases each year in India. The illness tends to be asymptomatic for the most part in its prior stages in this way making it almost difficult to identify. That is the reason early disease discovery has a significant impact in saving lives. An early recognition can allow a patient a superior opportunity to fix and recuperate. Innovation assumes a significant part in recognizing malignant growth productively. Numerous scientists have proposed various strategies in light of their examinations. As of late, to utilize PC innovation to tackle this issue, a few PC helped conclusion (CAD) strategies as well as framework have been proposed, created as well as arisen. Those frameworks utilize different Machine learning procedures as well as profound learning strategies, there likewise have been a few techniques dependent on picture handling based methods to foresee the harm level of malignant growth. Here, in this paper, the point will be focussed onto list, examine, look at and break down a few strategies in picture division, highlight extraction as well as different procedures to order and recognize cellular breakdown in the lungs in there early stages.The smart machines in future will utilize the profound learning calculations for the illness analysis, treatment arranging and medical procedure.

An Automated Vision-Based Deep Learning Model for Efficient Detection of Android Malware Attacks

Recently, cybersecurity experts and researchers have given special attention to developing cost-effective deep learning (DL)-based algorithms for Android malware detection (AMD) systems. However, the conventional AMD solutions necessitate extensive computations to achieve high accuracy in detecting Android malware apps. Consequently, there is a significant benefit in utilizing convolution neural networks (CNNs) in vision-based AMD applications to quickly and efficiently learn without prior stages of reverse engineering processes. Thus, this paper introduces an efficient and automated vision-based AMD model composed of 16 well-developed and fine-tuned CNN algorithms. This model precludes the need for a pre-designated features extraction process while generating accurate predictions of malware images with minimum cost and high detection speed. Such performance is achieved with colored or grayscale malware images, whether by using balanced or imbalanced datasets. Firstly, the bytecodes of the “classes.dex” files extracted from the Android benign and malware apps were converted to color and grayscale visual images before forwarding them to the developed CNN algorithms for classification. Then, the detection efficiency of the proposed AMD model was examined and evaluated using the imbalanced benchmark Leopard Android dataset that composes 14733 samples of malware apps and 2486 samples of benign apps. Finally, different experimental scenarios were conducted using balanced and imbalanced Android samples of color and grayscale images generated from the Leopard dataset; to extensively and sufficiently validate the detection and classification performance of the suggested model. Comprehensive assessment classification parameters in the evaluation experiments were applied to prove the high capability of the developed fine-tuned CNN algorithms in recognizing Android malware attacks with low computational overhead. As a result, the detection accuracy reached 99.40% for balanced samples and 98.05% for imbalanced samples. Furthermore, the proposed AMD model outperforms the existing approaches that utilize conventional vision-based algorithms and are tested on the same benchmark Android dataset.